$.ϴϴ5 first ask.
every ask after.
The model runs once. The behavioral profile lives in the file. Every follow-up is a lookup, not an inference. No new wattage.
A 6-minute conversation. One full pass to seal the file. After that, every question — about cadence, sentiment, who hesitated, who pushed — is a key lookup against a profile that is already there.
What the numbers mean
Where else the savings stack
- First-token latency on follow-up questions: up to 7x faster than running the model cold. Sub-second instead of 5–15 seconds.
- Per-token efficiency on cached follow-ups: ~64% lower than stock inference against the same content.
- Storage redundancy across codecs (the platforms keeping 5+ encodes of every title): 5%+ collapse at consolidation.
- Behavioral signal accuracy retained on cached lookups: ~99% of original-pass accuracy.
What 1% adoption looks like in 23
Re-running AI models on the same media is the silent climate cost of the AI era. We make the re-run optional. Conservative estimates against today’s grid intensity and data-center water baselines:
Estimates anchored to AI inference share of global electricity (224 baseline) and average data-center water-use intensity. Updated quarterly. Full energy and benchmarks report at v1. launch — methodology, baselines, third-party verification.
The point
Most platforms re-run the model on every question. We re-run nothing. Once the behavioral profile is sealed, the energy budget for re-questioning is structurally zero. Not a promise — a property of the file.